Laundry processing apparatus and control method thereof

ABSTRACT

Provided is a laundry processing apparatus including an inner tank in which a laundry is seated, a motor that transmits a rotational force to the inner tank, and a processor for controlling the motor to perform a washing cycle, a rinsing cycle, and a dewatering cycle. The processor receives an input of changes, during a last first set time, of a plurality of signal values indicating operation states of the inner tank and the motor in a set RPM section while accelerating RPM of the motor for performing the dewatering cycle and performs bubble detection and eccentricity detection based on the inputted changes of the plurality of signal values. The processor may perform the bubble detection and the eccentricity detection every second set time in the set RPM section.

CROSS-REFERENCE TO RELATED APPLICATIONS

Pursuant to 35 U.S.C. § 119(a), this application claims the benefit of earlier filing date and right of priority to Korean Patent Application No. 10-2019-0100112, filed on Aug. 16, 2019, the contents of which are hereby incorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION Field of the invention

The present disclosure relates to a laundry processing apparatus and a control method thereof, particularly to a laundry processing apparatus and a control method thereof capable of accurately detecting a bubble by using a characteristic pattern of a revolution per minute (RPM) acceleration section in a dewatering cycle.

Related Art

The laundry processing apparatus, which is one of the laundry processing apparatuses, means various apparatuses that process a fabric by applying a physical action and/or a chemical action to a laundry such as clothes and bedding.

The laundry processing apparatus includes an outer tank filled with washing water and an inner tank in which the fabric is seated and which is rotatably installed in the outer tank. A washing method of a general laundry processing apparatus includes a washing cycle for rotating the inner tank to wash a fabric, a rinsing cycle for removing the bubble used in the washing cycle, and a dewatering cycle for dehydrating the fabric using a centrifugal force of the inner tank.

However, if the rinsing is insufficiently performed in the rinsing cycle and the dewatering cycle is proceeded in a situation where the bubble remains, a phenomenon that the bubble flows back out of the laundry processing apparatus during an RPM acceleration may occur.

Therefore, in the related art, a threshold current value is set, and when the current value is greater than a set value, it is determined that there is a bubble, and an additional rinsing cycle is performed.

However, the current value is influenced not only by the bubble, but also by an eccentricity (unbalance) of the laundry.

Therefore, in the related art, it is not easy to distinguish whether the reason why the current value becomes greater than the set value is due to the bubble or due to the eccentricity.

Thus, in the related art, the laundry processing apparatus is controlled in such a manner that an RPM section in the dewatering cycle is separated, an eccentricity detection is performed up to specific RPM, for example, up to 320 RPM, and then bubble detection is performed.

However, according to the above control method, when the eccentricity is excessively limited, there is a problem that a dewatering time is increased to waste power, and when the set value is increased, there is a problem that the bubble detection may be failed.

SUMMARY OF THE INVENTION

The present disclosure is to solve the above problems, and an object thereof is to provide a laundry processing apparatus and a control method thereof, which can simultaneously perform bubble detection and eccentricity detection, can detect a bubble early to shorten a dewatering time, and can improve a washing quality by maximizing an accuracy of the bubble detection and minimizing an unnecessary rinsing due to a bubble backflow and an erroneous detection.

A laundry processing apparatus according to an embodiment of the present disclosure includes an inner tank in which a laundry is seated, a motor that transmits a rotational force to the inner tank, and a processor for controlling the motor to perform a washing cycle, a rinsing cycle, and a dewatering cycle. The processor receives an input of a change, during the last first set time, of a plurality of signal values indicating operation states of the inner tank and the motor in a set RPM section while accelerating RPM of the motor for performing the dewatering cycle and performs bubble detection and eccentricity detection based on the inputted changes of the plurality of signal values.

The processor may perform the bubble detection and the eccentricity detection every second set time in the set RPM section.

The processor may perform the dewatering cycle according to a first acceleration of maintaining first RPM for a predetermined time after accelerating the motor up to the first RPM, a second acceleration of maintaining second RPM for a predetermined time after accelerating the motor up to the second RPM higher than the first RPM, a third acceleration of maintaining third RPM for a predetermined time after accelerating the motor up to the third RPM higher than the second RPM, and a fourth acceleration of maintaining fourth RPM for a predetermined time after accelerating the motor up to the fourth RPM higher than the third RPM.

The set RPM section may be a section greater than the second RPM and smaller than the third RPM.

The first RPM may be 60 RPM, the second RPM may be 108 RPM, the third RPM may be 350 RPM, and the fourth RPM may be 1160 RPM.

The set RPM section may be greater than 108 RPM and smaller than 350 RPM.

The plurality of signal values may include current RPM (cRPM), request RPM (rRPM), a q-axis current (Iq), an unbalance (UB), a 3-axis Gyro-Diff, and a 3-axis Acc.-Diff, and RPM of the set RPM section and the first RPM to the fourth RPM may be respectively the request RPM.

The processor may learn a change pattern of the plurality of signal values through a neural network to perform bubble detection and eccentricity detection.

The first set time may be 2.4 sec and the second set time may be 60 ms.

A control method of a laundry processing apparatus for controlling the laundry processing apparatus having such a configuration may include starting a dewatering cycle, determining whether RPM of a motor is in a set RPM section, receiving an input of a change, during the last first set time, of a plurality of signal values indicating operation states of an inner tank and the motor of the laundry processing apparatus, when it is determined that the RPM of the motor is in the set RPM section in the determining, and performing bubble detection and eccentricity detection based on the changes of the plurality of signal values inputted in the receiving of the input of the change.

In this case, receiving of the input of the change and the performing of the bubble detection and eccentricity detection may be performed every second set time.

In the control method of the present embodiment, when the RPM of the motor exceeds the set RPM section, a final dewatering cycle may be performed by proceeding to the fourth acceleration.

The control method of the present embodiment may further include performing of fabric dispersion before the starting of the dewatering cycle.

In this case, when an eccentricity is detected in the performing of the bubble detection and eccentricity detection, the process may proceed to the performing of the fabric dispersion after stopping an operation of the motor, and when the bubble is detected in the performing of the bubble detection and eccentricity detection, the process may proceed to the performing of the fabric dispersion after removing the bubble.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a wireless communication system to which methods proposed in the specification are applicable.

FIG. 2 shows an example of a signal transmission/reception method in a wireless communication system.

FIG. 3 shows an example of basic operations of a user terminal and a 5G network in a 5G communication system.

FIG. 4 is a diagram showing a laundry processing apparatus according to an embodiment of the present disclosure.

FIG. 5 is a block diagram showing a main configuration of the laundry processing apparatus shown in FIG. 4.

FIG. 6 is a block diagram of an AI device provided in the laundry processing apparatus according to an embodiment of the present disclosure.

FIG. 7 is a flowchart showing a control method of a laundry processing apparatus according to the present disclosure.

FIG. 8 is a graph showing RPM in a dewatering cycle.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, embodiments of the disclosure will be described in detail with reference to the attached drawings. The same or similar components are given the same reference numbers and redundant description thereof is omitted. The suffixes “module” and “unit” of elements herein are used for convenience of description and thus can be used interchangeably and do not have any distinguishable meanings or functions. Further, in the following description, if a detailed description of known techniques associated with the present disclosure would unnecessarily obscure the gist of the present disclosure, detailed description thereof will be omitted. In addition, the attached drawings are provided for easy understanding of embodiments of the disclosure and do not limit technical spirits of the disclosure, and the embodiments should be construed as including all modifications, equivalents, and alternatives falling within the spirit and scope of the embodiments.

While terms, such as “first”, “second”, etc., may be used to describe various components, such components must not be limited by the above terms. The above terms are used only to distinguish one component from another.

When an element is “coupled” or “connected” to another element, it should be understood that a third element may be present between the two elements although the element may be directly coupled or connected to the other element. When an element is “directly coupled” or “directly connected” to another element, it should be understood that no element is present between the two elements.

The singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise.

In addition, in the specification, it will be further understood that the terms “comprise” and “include” specify the presence of stated features, integers, steps, operations, elements, components, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or combinations.

Hereinafter, 5G communication (5th generation mobile communication) required by an apparatus requiring AI processed information and/or an AI processor will be described through paragraphs A through G.

A. Example of Block Diagram of UE and 5G Network

FIG. 1 is a block diagram of a wireless communication system to which methods proposed in the disclosure are applicable.

Referring to FIG. 1, a device (AI device) including an AI module is defined as a first communication device (910 of FIG. 1), and a processor 911 can perform detailed AI operation.

A 5G network including another device (AI server) communicating with the AI device is defined as a second communication device (920 of FIG. 1), and a processor 921 can perform detailed AI operations.

The 5G network may be represented as the first communication device and the AI device may be represented as the second communication device.

For example, the first communication device or the second communication device may be a base station, a network node, a transmission terminal, a reception terminal, a wireless device, a wireless communication device, an autonomous device, or the like.

For example, the first communication device or the second communication device may be a base station, a network node, a transmission terminal, a reception terminal, a wireless device, a wireless communication device, a vehicle, a vehicle having an autonomous function, a connected car, a drone (Unmanned Aerial Vehicle, UAV), and AI (Artificial Intelligence) module, a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, an MR (Mixed Reality) device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a Fin Tech device (or financial device), a security device, a climate/environment device, a device associated with 5G services, or other devices associated with the fourth industrial revolution field.

For example, a terminal or user equipment (UE) may include a cellular phone, a smart phone, a laptop computer, a digital broadcast terminal, personal digital assistants (PDAs), a portable multimedia player (PMP), a navigation device, a slate PC, a tablet PC, an ultrabook, a wearable device (e.g., a smartwatch, a smart glass and a head mounted display (HMD)), etc. For example, the HMD may be a display device worn on the head of a user. For example, the HMD may be used to realize VR, AR or MR. For example, the drone may be a flying object that flies by wireless control signals without a person therein. For example, the VR device may include a device that implements objects or backgrounds of a virtual world. For example, the AR device may include a device that connects and implements objects or background of a virtual world to objects, backgrounds, or the like of a real world. For example, the MR device may include a device that unites and implements objects or background of a virtual world to objects, backgrounds, or the like of a real world. For example, the hologram device may include a device that implements 360-degree 3D images by recording and playing 3D information using the interference phenomenon of light that is generated by two lasers meeting each other which is called holography. For example, the public safety device may include an image repeater or an imaging device that can be worn on the body of a user. For example, the MTC device and the IoT device may be devices that do not require direct interference or operation by a person. For example, the MTC device and the IoT device may include a smart meter, a bending machine, a thermometer, a smart bulb, a door lock, various sensors, or the like. For example, the medical device may be a device that is used to diagnose, treat, attenuate, remove, or prevent diseases. For example, the medical device may be a device that is used to diagnose, treat, attenuate, or correct injuries or disorders. For example, the medial device may be a device that is used to examine, replace, or change structures or functions. For example, the medical device may be a device that is used to control pregnancy. For example, the medical device may include a device for medical treatment, a device for operations, a device for (external) diagnose, a hearing aid, an operation device, or the like. For example, the security device may be a device that is installed to prevent a danger that is likely to occur and to keep safety. For example, the security device may be a camera, a CCTV, a recorder, a black box, or the like. For example, the Fin Tech device may be a device that can provide financial services such as mobile payment.

Referring to FIG. 1, the first communication device 910 and the second communication device 920 include processors 911 and 921, memories 914 and 924, one or more Tx/Rx radio frequency (RF) modules 915 and 925, Tx processors 912 and 922, Rx processors 913 and 923, and antennas 916 and 926. The Tx/Rx module is also referred to as a transceiver. Each Tx/Rx module 915 transmits a signal through each antenna 926. The processor implements the aforementioned functions, processes and/or methods. The processor 921 may be related to the memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium. More specifically, the Tx processor 912 implements various signal processing functions with respect to L1 (i.e., physical layer) in DL (communication from the first communication device to the second communication device). The Rx processor implements various signal processing functions of L1 (i.e., physical layer).

UL (communication from the second communication device to the first communication device) is processed in the first communication device 910 in a way similar to that described in association with a receiver function in the second communication device 920. Each Tx/Rx module 925 receives a signal through each antenna 926. Each Tx/Rx module provides RF carriers and information to the Rx processor 923. The processor 921 may be related to the memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium.

B. Signal Transmission/Reception Method in Wireless Communication System

FIG. 2 is a diagram showing an example of a signal transmission/reception method in a wireless communication system.

Referring to FIG. 2, when a UE is powered on or enters a new cell, the UE performs an initial cell search operation such as synchronization with a BS (S201). For this operation, the UE can receive a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the BS to synchronize with the BS and acquire information such as a cell ID. In LTE and NR systems, the P-SCH and S-SCH are respectively called a primary synchronization signal (PSS) and a secondary synchronization signal (SSS). After initial cell search, the UE can acquire broadcast information in the cell by receiving a physical broadcast channel (PBCH) from the BS. Further, the UE can receive a downlink reference signal (DL RS) in the initial cell search step to check a downlink channel state. After initial cell search, the UE can acquire more detailed system information by receiving a physical downlink shared channel (PDSCH) according to a physical downlink control channel (PDCCH) and information included in the PDCCH (S202).

Meanwhile, when the UE initially accesses the BS or has no radio resource for signal transmission, the UE can perform a random access procedure (RACH) for the BS (steps S203 to S206). To this end, the UE can transmit a specific sequence as a preamble through a physical random access channel (PRACH) (S203 and S205) and receive a random access response (RAR) message for the preamble through a PDCCH and a corresponding PDSCH (S204 and S206). In the case of a contention-based RACH, a contention resolution procedure may be additionally performed.

After the UE performs the above-described process, the UE can perform PDCCH/PDSCH reception (S207) and physical uplink shared channel (PUSCH)/physical uplink control channel (PUCCH) transmission (S208) as normal uplink/downlink signal transmission processes. Particularly, the UE receives downlink control information (DCI) through the PDCCH. The UE monitors a set of PDCCH candidates in monitoring occasions set for one or more control element sets (CORESET) on a serving cell according to corresponding search space configurations. A set of PDCCH candidates to be monitored by the UE is defined in terms of search space sets, and a search space set may be a common search space set or a UE-specific search space set. CORESET includes a set of (physical) resource blocks having a duration of one to three OFDM symbols. A network can configure the UE such that the UE has a plurality of CORESETs. The UE monitors PDCCH candidates in one or more search space sets. Here, monitoring means attempting decoding of PDCCH candidate(s) in a search space. When the UE has successfully decoded one of PDCCH candidates in a search space, the UE determines that a PDCCH has been detected from the PDCCH candidate and performs PDSCH reception or PUSCH transmission on the basis of DCI in the detected PDCCH. The PDCCH can be used to schedule DL transmissions over a PDSCH and UL transmissions over a PUSCH. Here, the DCI in the PDCCH includes downlink assignment (i.e., downlink grant (DL grant)) related to a physical downlink shared channel and including at least a modulation and coding format and resource allocation information, or an uplink grant (UL grant) related to a physical uplink shared channel and including a modulation and coding format and resource allocation information.

An initial access (IA) procedure in a 5G communication system will be additionally described with reference to FIG. 2.

The UE can perform cell search, system information acquisition, beam alignment for initial access, and DL measurement on the basis of an SSB. The SSB is interchangeably used with a synchronization signal/physical broadcast channel (SS/PBCH) block.

The SSB includes a PSS, an SSS and a PBCH. The SSB is configured in four consecutive OFDM symbols, and a PSS, a PBCH, an SSS/PBCH or a PBCH is transmitted for each OFDM symbol. Each of the PSS and the SSS includes one OFDM symbol and 127 subcarriers, and the PBCH includes 3 OFDM symbols and 576 subcarriers.

Cell search refers to a process in which a UE acquires time/frequency synchronization of a cell and detects a cell identifier (ID) (e.g., physical layer cell ID (PCI)) of the cell. The PSS is used to detect a cell ID in a cell ID group and the SSS is used to detect a cell ID group. The PBCH is used to detect an SSB (time) index and a half-frame.

There are 336 cell ID groups and there are 3 cell IDs per cell ID group. A total of 1008 cell IDs are present. Information on a cell ID group to which a cell ID of a cell belongs is provided/acquired through an SSS of the cell, and information on the cell ID among 336 cell ID groups is provided/acquired through a PSS.

The SSB is periodically transmitted in accordance with SSB periodicity. A default SSB periodicity assumed by a UE during initial cell search is defined as 20 ms. After cell access, the SSB periodicity can be set to one of {5 ms, 10 ms, 20 ms, 40 ms, 80 ms, 160 ms} by a network (e.g., a BS).

Next, acquisition of system information (SI) will be described.

SI is divided into a master information block (MIB) and a plurality of system information blocks (SIBs). SI other than the MIB may be referred to as remaining minimum system information. The MIB includes information/parameter for monitoring a PDCCH that schedules a PDSCH carrying SIB1 (SystemInformationBlock1) and is transmitted by a BS through a PBCH of an SSB. SIB1 includes information related to availability and scheduling (e.g., transmission periodicity and SI-window size) of the remaining SIBs (hereinafter, SIBx, x is an integer equal to or greater than 2). SiBx is included in an SI message and transmitted over a PDSCH. Each SI message is transmitted within a periodically generated time window (i.e., SI-window).

A random access (RA) procedure in a 5G communication system will be additionally described with reference to FIG. 2.

A random access procedure is used for various purposes. For example, the random access procedure can be used for network initial access, handover, and UE-triggered UL data transmission. A UE can acquire UL synchronization and UL transmission resources through the random access procedure. The random access procedure is classified into a contention-based random access procedure and a contention-free random access procedure. A detailed procedure for the contention-based random access procedure is as follows.

A UE can transmit a random access preamble through a PRACH as Msg1 of a random access procedure in UL. Random access preamble sequences having different two lengths are supported. A long sequence length 839 is applied to subcarrier spacings of 1.25 kHz and 5 kHz and a short sequence length 139 is applied to subcarrier spacings of 15 kHz, 30 kHz, 60 kHz and 120 kHz.

When a BS receives the random access preamble from the UE, the BS transmits a random access response (RAR) message (Msg2) to the UE. A PDCCH that schedules a PDSCH carrying a RAR is CRC masked by a random access (RA) radio network temporary identifier (RNTI) (RA-RNTI) and transmitted. Upon detection of the PDCCH masked by the RA-RNTI, the UE can receive a RAR from the PDSCH scheduled by DCI carried by the PDCCH. The UE checks whether the RAR includes random access response information with respect to the preamble transmitted by the UE, that is, Msg1. Presence or absence of random access information with respect to Msg1 transmitted by the UE can be determined according to presence or absence of a random access preamble ID with respect to the preamble transmitted by the UE. If there is no response to Msg1, the UE can retransmit the RACH preamble less than a predetermined number of times while performing power ramping. The UE calculates PRACH transmission power for preamble retransmission on the basis of most recent pathloss and a power ramping counter.

The UE can perform UL transmission through Msg3 of the random access procedure over a physical uplink shared channel on the basis of the random access response information. Msg3 can include an RRC connection request and a UE ID. The network can transmit Msg4 as a response to Msg3, and Msg4 can be handled as a contention resolution message on DL. The UE can enter an RRC connected state by receiving Msg4.

C. Basic Operation Between Autonomous Vehicles Using 5G Communication

FIG. 3 shows an example of basic operations of an autonomous vehicle and a 5G network in a 5G communication system.

The autonomous vehicle transmits specific information to the 5G network (S1). The specific information may include autonomous driving related information. In addition, the 5G network can determine whether to remotely control the vehicle (S2). Here, the 5G network may include a server or a module which performs remote control related to autonomous driving. In addition, the 5G network can transmit information (or signal) related to remote control to the autonomous vehicle (S3).

D. Applied Operations Between Autonomous Vehicle and 5G Network in 5G Communication System

Hereinafter, the operation of an autonomous vehicle using 5G communication will be described in more detail with reference to wireless communication technology (BM procedure, URLLC, mMTC, etc.) described in FIGS. 1 and 2.

First, a basic procedure of an applied operation to which a method proposed by the present disclosure which will be described later and eMBB of 5G communication are applied will be described.

As in steps S1 and S3 of FIG. 3, the autonomous vehicle performs an initial access procedure and a random access procedure with the 5G network prior to step S1 of FIG. 3 in order to transmit/receive signals, information and the like to/from the 5G network.

More specifically, the autonomous vehicle performs an initial access procedure with the 5G network on the basis of an SSB in order to acquire DL synchronization and system information. A beam management (BM) procedure and a beam failure recovery procedure may be added in the initial access procedure, and quasi-co-location (QCL) relation may be added in a process in which the autonomous vehicle receives a signal from the 5G network.

In addition, the autonomous vehicle performs a random access procedure with the 5G network for UL synchronization acquisition and/or UL transmission. The 5G network can transmit, to the autonomous vehicle, a UL grant for scheduling transmission of specific information. Accordingly, the autonomous vehicle transmits the specific information to the 5G network on the basis of the UL grant. In addition, the 5G network transmits, to the autonomous vehicle, a DL grant for scheduling transmission of 5G processing results with respect to the specific information. Accordingly, the 5G network can transmit, to the autonomous vehicle, information (or a signal) related to remote control on the basis of the DL grant.

Next, a basic procedure of an applied operation to which a method proposed by the present disclosure which will be described later and URLLC of 5G communication are applied will be described.

As described above, an autonomous vehicle can receive DownlinkPreemption IE from the 5G network after the autonomous vehicle performs an initial access procedure and/or a random access procedure with the 5G network. Then, the autonomous vehicle receives DCI format 2_1 including a preemption indication from the 5G network on the basis of DownlinkPreemption IE. The autonomous vehicle does not perform (or expect or assume) reception of eMBB data in resources (PRBs and/or OFDM symbols) indicated by the preemption indication. Thereafter, when the autonomous vehicle needs to transmit specific information, the autonomous vehicle can receive a UL grant from the 5G network.

The above-described 5G communication technology can be combined with methods proposed in the present disclosure which will be described later and applied or can complement the methods proposed in the present disclosure to make technical features of the methods concrete and clear.

Laundry Processing Apparatus

FIG. 4 is a diagram showing a laundry processing apparatus according to an embodiment of the present disclosure. FIG. 5 is a block diagram showing a main configuration of the laundry processing apparatus shown in FIG. 4. FIG. 6 is a block diagram of an AI device provided in the laundry processing apparatus according to an embodiment of the present disclosure.

Referring to the drawings, the laundry processing apparatus according to an embodiment of the present disclosure includes a processor 100, hardware 200, and a user interface 400.

The processor 100 controls the hardware 200 according to information input through the user interface 400, thereby controlling the overall driving of the laundry processing apparatus.

The processor 100 may also control an operation of the hardware 200 based on a laundry image acquired by an image acquisition unit (not shown).

In this case, the processor 100 may obtain fabric classification information or fabric dispersion information from the laundry image and control the operation of the hardware 200 based on the fabric classification information or the fabric dispersion information. The fabric classification information may refer to information on a type, a material, and the like of the laundry, and particularly refer to moisture content information of the laundry. The fabric dispersion information may refer to information on a degree of placement of the laundry or information on a height of the laundry seated in an inner tank 211.

The processor 100 may learn the fabric classification information to predict a vibration degree of the inner tank 211 that may occur in the dewatering cycle, and to vary the RPM of the motor 220 in the dewatering cycle depending on the vibration degree of the inner tank 211. For example, when the fabric classification information is determined to be the laundry that may cause short circuit, the processor 100 may control to lower the RPM of the motor 220 in the dewatering cycle.

The hardware 200 may include a washing tank 210, the motor 220, a water supply valve 230, a heater 240, and the like.

The washing tank 210 includes an outer tank 213 accommodating washing water, and the inner tank 211 disposed inside the outer tank 213 and in which a laundry is seated, and rotates using a rotational force provided from the motor 220. The water supply valve 230 controls the supply of the washing water. The heater 240 heats the water supplied in the washing tank.

The hardware 200 may further include a main body 1, a thermistor 4 for measuring a temperature of the washing water supplied to the inner tank 211, a detergent container 5 for injecting detergent, a water supply pipe 6 connected to the detergent container for supplying the washing water or washing water mixed with the detergent of the detergent container, a drain pipe 7 for discharging the washing water used for the washing cycle to the outside, a pump 8 connected to an end of the drain pipe for forcibly pumping the washing water, a drain hose 9, and a belt 11 for transmitting a driving force of the motor to the inner tank.

The user interface 400 may include a power input unit 410, a start input unit 420, a course selector 430, an option selector 440, a display 450, and a speaker 460.

The power input unit 410 provides a means for controlling the on and off of the main power of the laundry processing apparatus.

The start input unit 420 provides a means for controlling the start of the washing cycle, the rinsing cycle, or the dewatering cycle.

The course selector 430 provides a means for selecting a type of the washing cycle, the rinsing cycle, or the dewatering cycle.

The option selector 440 provides a means for selecting detailed options for the washing cycle, the rinsing cycle, or the dewatering cycle. For example, the option selector 440 may be a means for selecting options such as water temperature, time, and reservation.

The display 450 may display an operation state of the laundry processing apparatus, or display course information selected through the course selector 430 by the user or option information selected through the option selector 440 by the user. The speaker 460 outputs the operation state of a laundry processing apparatus or a situation for a specific event as a voice signal. The specific event may be a situation of either a fabric dispersion control based on a fabric image or an RPM control.

Referring to FIG. 6, the AI device 20 may include an electronic device including an AI module capable of performing AI processing or a server including an AI module.

In addition, the AI device 20 may be included in at least a part of the configuration of the laundry processing apparatus illustrated in FIGS. 4 and 5 to be provided to perform at least a part of the AI processing together.

The AI processing may include all operations related to the processor 100 of the laundry processing apparatus illustrated in FIG. 4. For example, the laundry processing apparatus may perform AI processing of the laundry image, the fabric classification information, or the fabric dispersion information to perform processing or determination, and a control signal generation operation.

The AI device 20 may be a client device that directly uses the result of the AI processing or may be a device in a cloud environment that provides the result of the AI processing to another device.

The AI device 20 may be a computing device capable of learning a neural network, and may be implemented as various electronic devices such as a server, a desktop PC, a notebook PC, a tablet PC, and the like.

The AI device 20 may include an AI processor 21, a memory 25, and/or a transceiver 27.

The AI processor 21 may train the ANN based on the program stored in the memory 25. In particular, the AI processor 21 may train a neural network for recognizing relevant data of the washer 10. The neural network for recognizing the relevant data of the washer 10 may be designed to mimic the human brain on the computer and may include a plurality of weighted network nodes which mimic the neurons of the human neural network. The plurality of network nodes can transmit and receive data in accordance with each connection relationship to simulate the synaptic activity of neurons in which neurons transmit and receive signals through synapses. Here, the neural network may include a deep learning model developed from a neural network model. In the deep learning model, a plurality of network nodes is positioned in different layers and can transmit and receive data in accordance with a convolution connection relationship. The neural network, for example, includes various deep learning techniques such as deep neural networks (DNN), convolutional deep neural networks(CNN), recurrent neural networks (RNN), a restricted Boltzmann machine (RBM), deep belief networks (DBN), and a deep Q-network, and can be applied to fields such as computer vision, voice recognition, natural language processing, and voice/signal processing.

Meanwhile, a processor that performs the functions described above may be a general purpose processor (e.g., a CPU), but may be an AI-only processor (e.g., a GPU) for artificial intelligence learning.

The memory 25 can store various programs and data for the operation of the AI device 20. The memory 25 may be a nonvolatile memory, a volatile memory, a flash-memory, a hard disk drive (HDD), a solid state drive (SDD), or the like. The memory 25 is accessed by the AI processor 21 and reading-out/recording/correcting/deleting/updating, etc. of data by the AI processor 21 can be performed. Further, the memory 25 can store a neural network model (e.g., a deep learning model 26) generated through a learning algorithm for data classification/recognition according to an embodiment of the present disclosure.

Meanwhile, the AI processor 21 may include a data learning unit 22 that learns a neural network for data classification/recognition. The data learning unit 22 can learn references about what learning data are used and how to classify and recognize data using the learning data in order to determine data classification/recognition. The data learning unit 22 can learn a deep learning model by acquiring learning data to be used for learning and by applying the acquired learning data to the deep learning model.

The data learning unit 22 may be manufactured in the type of at least one hardware chip and mounted on the AI device 20. For example, the data learning unit 22 may be manufactured in a hardware chip type only for artificial intelligence, and may be manufactured as a part of a general purpose processor (CPU) or a graphics processing unit (GPU) and mounted on the AI device 20. Further, the data learning unit 22 may be implemented as a software module. When the data leaning unit 22 is implemented as a software module (or a program module including instructions), the software module may be stored in non-transitory computer readable media that can be read through a computer. In this case, at least one software module may be provided by an OS (operating system) or may be provided by an application.

The data learning unit 22 may include a learning data acquiring unit 23 and a model learning unit 24.

The learning data acquiring unit 23 can acquire learning data required for a neural network model for classifying and recognizing data. For example, the learning data acquiring unit 23 can acquire, as learning data, vehicle data and/or sample data to be input to a neural network model.

The model learning unit 24 can perform learning such that a neural network model has a determination reference about how to classify predetermined data, using the acquired learning data. In this case, the model learning unit 24 can train a neural network model through supervised learning that uses at least some of learning data as a determination reference. Alternatively, the model learning data 24 can train a neural network model through unsupervised learning that finds out a determination reference by performing learning by itself using learning data without supervision. Further, the model learning unit 24 can train a neural network model through reinforcement learning using feedback about whether the result of situation determination according to learning is correct. Further, the model learning unit 24 can train a neural network model using a learning algorithm including error back-propagation or gradient decent.

When a neural network model is learned, the model learning unit 24 can store the learned neural network model in the memory. The model learning unit 24 may store the learned neural network model in the memory of a server connected with the AI device 20 through a wire or wireless network.

The data learning unit 22 may further include a learning data preprocessor (not shown) and a learning data selector (not shown) to improve the analysis result of a recognition model or reduce resources or time for generating a recognition model.

The learning data preprocessor can preprocess acquired data such that the acquired data can be used in learning for situation determination. For example, the learning data preprocessor can process acquired data in a predetermined format such that the model learning unit 24 can use learning data acquired for learning for image recognition.

Further, the learning data selector can select data for learning from the learning data acquired by the learning data acquiring unit 23 or the learning data preprocessed by the preprocessor. The selected learning data can be provided to the model learning unit 24. For example, the learning data selector can select only data for objects included in a specific area as learning data by detecting the specific area in an image acquired through a camera of a vehicle.

Further, the data learning unit 22 may further include a model estimator (not shown) to improve the analysis result of a neural network model.

The model estimator inputs estimation data to a neural network model, and when an analysis result output from the estimation data does not satisfy a predetermined reference, it can make the model learning unit 22 perform learning again. In this case, the estimation data may be data defined in advance for estimating a recognition model. For example, when the number or ratio of estimation data with an incorrect analysis result of the analysis result of a recognition model learned with respect to estimation data exceeds a predetermined threshold, the model estimator can estimate that a predetermined reference is not satisfied.

The transceiver 27 can transmit the AI processing result by the AI processor 21 to an external electronic device.

Here, the external electronic device may be defined as an autonomous vehicle. Further, the AI device 20 may be defined as another vehicle or a 5G network that communicates with the autonomous vehicle. Meanwhile, the AI device 20 may be implemented by being functionally embedded in an autonomous module included in a vehicle. Further, the 5G network may include a server or a module that performs control related to autonomous driving.

Meanwhile, the AI device 20 shown in FIG. 5 was functionally separately described into the AI processor 21, the memory 25, the transceiver 27, etc., but it should be noted that the aforementioned components may be integrated in one module and referred to as an AI module.

Control Method of Laundry Processing Apparatus

FIG. 7 is a flowchart illustrating a control method of a laundry processing apparatus according to the present disclosure, and FIG. 8 is a graph showing RPM in the dewatering cycle.

Referring to the drawings, the control method of the laundry processing apparatus according to the present disclosure includes accelerating the RPM after performing the fabric dispersion.

Specifically, when the laundry processing apparatus enters the dewatering cycle, the processor 100 performs a first acceleration of increasing a rotational speed of the motor 220 to first RPM (RPM1), and then maintains the rotational speed of the motor 220 for a predetermined time.

In addition, after the processor 100 performs a second acceleration of increasing the rotational speed of the motor 220 from the first RPM (RPM1) to second RPM (RPM2), the processor 100 maintains the rotational speed of the motor 220 for a predetermined time.

Similarly, after the processor 100 performs a third acceleration of increasing the rotational speed of the motor 220 from the second RPM (RPM2) to third RPM (RPM3), the processor 100 maintains the rotational speed of the motor 220 for a predetermined time.

After the processor 100 performs a fourth acceleration of increasing the rotational speed of the motor 220 from the third RPM (RPM3) to fourth RPM (RPM4), the processor 100 maintains the rotational speed of the motor 220 for a predetermined time.

At this time, the first RPM (RPM1) may be 60 RPM, the second RPM (RPM2) may be 108 RPM, the third RPM (RPM3) may be 350 RPM, and the fourth RPM (RPM4) may be 1160 RPM. The first RPM to the fourth RPM may be changed to the other values as necessary.

When performing the dewatering cycle by these first to fourth accelerations, the processor 100 may receive an input of a change, during the last first setting time, of a plurality of signal values indicating the driving states of the inner tank 211 and the motor 220 in a set RPM section (which is greater than 108 RPM and smaller than 350 RPM) corresponding to the third acceleration, and may perform the bubble detection and the eccentricity detection based on the inputted changes of the plurality of the signal values.

In addition, the processor 100 may perform the bubble detection and the eccentricity detection every second set time in the set RPM section.

In this regard, the plurality of signal values inputted to the processor 100 may include current RPM (cRPM), request RPM (rRPM), a q-axis current (Iq), an unbalance (UB), a 3-axis Gyro-Diff, and a 3-axis Acc.-Diff.

The first set time may be 2.4 sec, and the second set time may be 60 ms. The first set time and the second set time may be changed to different values as necessary.

At this time, the RPM of the set RPM section and the first RPM to the fourth RPM may be respectively the request RPM (rRPM).

Accordingly, the processor 100 may perform the bubble detection and the eccentricity detection every 60 ms by inputting the changes of the 10 signal values in the set RPM section during the last 2.4 seconds.

For example, the processor 100 may train a bubble detection neural network using a label indicating whether the bubble remains by inputting the changes of the 10 signal values.

Therefore, the processing efficiency can be increased by performing the bubble detection using the same input as that of the eccentricity detection.

When the request RPM (rRPM) exceeds the set RPM section, the processor 100 may proceed to the fourth acceleration to perform a final dewatering cycle.

That is, when the request RPM (rRPM) is 350 RPM, the processor 100 may proceed to the fourth acceleration to perform the final dewatering cycle.

When the eccentricity is detected, the processor 100 may stop the driving of the motor 220 and then proceed to perform the fabric dispersion, and when the bubble is detected, the processor 100 may perform the removing of the bubble and then proceed to perform the fabric dispersion.

The configuration described in the present specification should not be limitedly interpreted in all respects, but should be considered as illustrative. The scope of the disclosure should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the disclosure are included in the scope of the disclosure.

According to the present disclosure, the bubble detection and the eccentricity detection may be performed simultaneously in the RPM acceleration section for performing the dewatering cycle.

Therefore, it is possible to detect the bubble early to shorten the dewatering time, and it is possible to maximize an accuracy of the bubble detection to minimize an unnecessary rinsing due to a backflow and an erroneous detection thereby improving a washing quality. 

What is claimed is:
 1. A laundry processing apparatus comprising: an inner tank in which a laundry is seated; a motor that transmits a rotational force to the inner tank; and a processor for controlling the motor to perform a washing cycle, a rinsing cycle, and a dewatering cycle, wherein the processor receives an input of a change, during the last first set time, of a plurality of signal values indicating operation states of the inner tank and the motor in a set RPM section while accelerating RPM of the motor for performing the dewatering cycle and performs bubble detection and eccentricity detection based on the inputted changes of the plurality of signal values.
 2. The laundry processing apparatus according to claim 1, wherein the processor performs the bubble detection and the eccentricity detection every second set time in the set RPM section.
 3. The laundry processing apparatus according to claim 2, wherein the processor performs the dewatering cycle according to a first acceleration of maintaining first RPM for a predetermined time after accelerating the motor up to the first RPM, a second acceleration of maintaining second RPM for a predetermined time after accelerating the motor up to the second RPM higher than the first RPM, a third acceleration of maintaining third RPM for a predetermined time after accelerating the motor up to the third RPM higher than the second RPM, and a fourth acceleration of maintaining fourth RPM for a predetermined time after accelerating the motor up to the fourth RPM higher than the third RPM.
 4. The laundry processing apparatus according to claim 3, wherein the set RPM section is a section greater than the second RPM and smaller than the third RPM.
 5. The laundry processing apparatus according to claim 4, wherein the first RPM is 60 RPM, the second RPM is 108 RPM, the third RPM is 350 RPM, and the fourth RPM is 1160 RPM.
 6. The laundry processing apparatus according to claim 2, wherein the set RPM section is greater than 108 RPM and smaller than 350 RPM.
 7. The laundry processing apparatus according to claim 1, wherein the plurality of signal values include current RPM (cRPM), request RPM (rRPM), a q-axis current (Iq), an unbalance (UB), a 3-axis Gyro-Diff, and a 3-axis Acc.-Diff, and RPM of the set RPM section and the first RPM to the fourth RPM are respectively the request RPM.
 8. The laundry processing apparatus according to claim 7, wherein the processor learns a change pattern of the plurality of signal values through a neural network to perform bubble detection and eccentricity detection.
 9. The laundry processing apparatus according to claim 8, wherein the first set time is 2.4 sec and the second set time is 60 ms.
 10. A control method of a laundry processing apparatus for controlling the laundry processing apparatus, the method comprising: starting a dewatering cycle; determining whether RPM of a motor is in a set RPM section; receiving an input of a change, during the last first set time, of a plurality of signal values indicating operation states of an inner tank and the motor of the laundry processing apparatus, when it is determined that the RPM of the motor is in the set RPM section in the determining; and performing bubble detection and eccentricity detection based on the changes of the plurality of signal values inputted in the receiving of the input of the change.
 11. The control method of a laundry processing apparatus according to claim 10, wherein the receiving of the input of the change and the performing of the bubble detection and eccentricity detection are performed every second set time.
 12. The control method of a laundry processing apparatus according to claim 11, wherein the dewatering cycle is performed according to a first acceleration of maintaining first RPM for a predetermined time after accelerating the motor up to the first RPM, a second acceleration of maintaining second RPM for a predetermined time after accelerating the motor up to the second RPM higher than the first RPM, a third acceleration of maintaining third RPM for a predetermined time after accelerating the motor up to the third RPM higher than the second RPM, and a fourth acceleration of maintaining fourth RPM for a predetermined time after accelerating the motor up to the fourth RPM higher than the third RPM.
 13. The control method of a laundry processing apparatus according to claim 12, wherein the set RPM section is a section greater than the second RPM and smaller than the third RPM.
 14. The control method of a laundry processing apparatus according to claim 13, wherein the first RPM is 60 RPM, the second RPM is 108 RPM, the third RPM is 350 RPM, and the fourth RPM is 1160 RPM.
 15. The control method of a laundry processing apparatus according to claim 13, wherein when the RPM of the motor exceeds the set RPM section, a final dewatering cycle is performed by proceeding to the fourth acceleration.
 16. The control method of a laundry processing apparatus according to claim 10, wherein the plurality of signal values include current RPM (cRPM), request RPM (rRPM), a q-axis current (Iq), an unbalance (UB), a 3-axis Gyro-Diff, and a 3-axis Acc.-Diff, and RPM of the set RPM section and the first RPM to the fourth RPM are respectively the request RPM.
 17. The control method of a laundry processing apparatus according to claim 16, wherein bubble detection and eccentricity detection are performed by learning a change pattern of the plurality of signal values through a neural network.
 18. The control method of a laundry processing apparatus according to claim 17, wherein the first set time is 2.4 sec and the second set time is 60 ms.
 19. The control method of a laundry processing apparatus according to claim 18, further comprising: performing fabric dispersion before the starting of the dewatering cycle; wherein when an eccentricity is detected in the performing of the bubble detection and eccentricity detection, the process proceeds to the performing of the fabric dispersion after stopping an operation of the motor, and when the bubble is detected in the performing of the bubble detection and eccentricity detection, the process proceeds to the performing of the fabric dispersion after removing the bubble. 